Python chainer.links.DilatedConvolution2D() Examples

The following are 8 code examples of chainer.links.DilatedConvolution2D(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module chainer.links , or try the search function .
Example #1
Source File: conv_2d_activ.py    From chainer-compiler with MIT License 6 votes vote down vote up 
def __init__(self, in_channels, out_channels, ksize=None,
                 stride=1, pad=0, dilate=1, nobias=False, initialW=None,
                 initial_bias=None, activ=relu):
        if ksize is None:
            out_channels, ksize, in_channels = in_channels, out_channels, None

        self.activ = activ
        super(Conv2DActiv, self).__init__()
        with self.init_scope():
            if dilate > 1:
                self.conv = DilatedConvolution2D(
                    in_channels, out_channels, ksize, stride, pad, dilate,
                    nobias, initialW, initial_bias)
            else:
                self.conv = Convolution2D(
                    in_channels, out_channels, ksize, stride, pad,
                    nobias, initialW, initial_bias)
Example #2
Source File: ssd_vgg16.py    From chainercv with MIT License 6 votes vote down vote up 
def __init__(self):
        super(VGG16, self).__init__()
        with self.init_scope():
            self.conv1_1 = L.Convolution2D(64, 3, pad=1)
            self.conv1_2 = L.Convolution2D(64, 3, pad=1)

            self.conv2_1 = L.Convolution2D(128, 3, pad=1)
            self.conv2_2 = L.Convolution2D(128, 3, pad=1)

            self.conv3_1 = L.Convolution2D(256, 3, pad=1)
            self.conv3_2 = L.Convolution2D(256, 3, pad=1)
            self.conv3_3 = L.Convolution2D(256, 3, pad=1)

            self.conv4_1 = L.Convolution2D(512, 3, pad=1)
            self.conv4_2 = L.Convolution2D(512, 3, pad=1)
            self.conv4_3 = L.Convolution2D(512, 3, pad=1)
            self.norm4 = Normalize(512, initial=initializers.Constant(20))

            self.conv5_1 = L.DilatedConvolution2D(512, 3, pad=1)
            self.conv5_2 = L.DilatedConvolution2D(512, 3, pad=1)
            self.conv5_3 = L.DilatedConvolution2D(512, 3, pad=1)

            self.conv6 = L.DilatedConvolution2D(1024, 3, pad=6, dilate=6)
            self.conv7 = L.Convolution2D(1024, 1)
Example #3
Source File: conv_2d_activ.py    From chainercv with MIT License 6 votes vote down vote up 
def __init__(self, in_channels, out_channels, ksize=None,
                 stride=1, pad=0, dilate=1, nobias=False, initialW=None,
                 initial_bias=None, activ=relu):
        if ksize is None:
            out_channels, ksize, in_channels = in_channels, out_channels, None

        self.activ = activ
        super(Conv2DActiv, self).__init__()
        with self.init_scope():
            if dilate > 1:
                self.conv = DilatedConvolution2D(
                    in_channels, out_channels, ksize, stride, pad, dilate,
                    nobias, initialW, initial_bias)
            else:
                self.conv = Convolution2D(
                    in_channels, out_channels, ksize, stride, pad,
                    nobias, initialW, initial_bias)
Example #4
Source File: resnet101.py    From chainer-fcis with MIT License 6 votes vote down vote up 
def __init__(self, in_size, out_size, ch, stride=1):
        super(DilatedBottleNeckA, self).__init__()
        initialW = chainer.initializers.HeNormal()

        with self.init_scope():
            self.conv1 = L.Convolution2D(
                in_size, ch, 1, stride, 0, initialW=initialW, nobias=True)
            self.bn1 = L.BatchNormalization(ch, eps=self.eps)
            self.conv2 = L.DilatedConvolution2D(
                ch, ch, 3, 1, 2, dilate=2,
                initialW=initialW, nobias=True)
            self.bn2 = L.BatchNormalization(ch, eps=self.eps)
            self.conv3 = L.Convolution2D(
                ch, out_size, 1, 1, 0, initialW=initialW, nobias=True)
            self.bn3 = L.BatchNormalization(out_size, eps=self.eps)

            self.conv4 = L.Convolution2D(
                in_size, out_size, 1, stride, 0,
                initialW=initialW, nobias=True)
            self.bn4 = L.BatchNormalization(out_size)
Example #5
Source File: module.py    From fpl with MIT License 5 votes vote down vote up 
def __init__(self, nb_in, nb_out, ksize=3, dilate=1, no_bn=False):
        super(DConv_BN, self).__init__()
        self.no_bn = no_bn
        with self.init_scope():
            self.conv = L.DilatedConvolution2D(nb_in, nb_out, ksize=(ksize, 1), pad=(dilate, 0), dilate=(dilate, 1))
            if not no_bn:
                self.bn = L.BatchNormalization(nb_out)
Example #6
Source File: test_dilated_convolution_2d.py    From chainer with MIT License 5 votes vote down vote up 
def setUp(self):
        self.link = links.DilatedConvolution2D(
            3, 2, 3, stride=2, pad=2, dilate=2)
        b = self.link.b.data
        b[...] = numpy.random.uniform(-1, 1, b.shape)
        self.link.cleargrads()

        self.x = numpy.random.uniform(-1, 1,
                                      (2, 3, 4, 3)).astype(numpy.float32)
        self.gy = numpy.random.uniform(-1, 1,
                                       (2, 2, 2, 2)).astype(numpy.float32)
Example #7
Source File: test_dilated_convolution_2d.py    From chainer with MIT License 5 votes vote down vote up 
def setUp(self):
        self.link = links.DilatedConvolution2D(*self.args, **self.kwargs)
        self.x = numpy.random.uniform(-1, 1,
                                      (2, 3, 4, 3)).astype(numpy.float32)
        self.link(chainer.Variable(self.x))
        b = self.link.b.data
        b[...] = numpy.random.uniform(-1, 1, b.shape)
        self.link.cleargrads()
        self.gy = numpy.random.uniform(-1, 1,
                                       (2, 2, 2, 2)).astype(numpy.float32)
Example #8
Source File: resnet101.py    From chainer-fcis with MIT License 5 votes vote down vote up 
def __init__(self, in_size, ch):
        super(DilatedBottleNeckB, self).__init__()
        initialW = chainer.initializers.HeNormal()

        with self.init_scope():
            self.conv1 = L.Convolution2D(
                in_size, ch, 1, 1, 0, initialW=initialW, nobias=True)
            self.bn1 = L.BatchNormalization(ch, eps=self.eps)
            self.conv2 = L.DilatedConvolution2D(
                ch, ch, 3, 1, 2, dilate=2,
                initialW=initialW, nobias=True)
            self.bn2 = L.BatchNormalization(ch, eps=self.eps)
            self.conv3 = L.Convolution2D(
                ch, in_size, 1, 1, 0, initialW=initialW, nobias=True)
            self.bn3 = L.BatchNormalization(in_size, eps=self.eps)